Due to technological innovations in data storage capacity, current enterprises try to capture and store as much data as possible when conducting enterprise-level activities. The rate and richness (e.g., depth) at which this data may be searched and/or manipulated may be inhibited by costs related to executing enterprise-level data processing tasks (e.g., queries) over enterprise-level data stores (e.g., over a terabyte in storage memory space). These tasks often consume a considerable portion of total processing time and/or available computing resources, which can encumber enterprise productivity. It is impractical, for example, to delay other information technology processes and/or wait overnight for database queries to conclude and for results to return. While present day enterprises routinely fund projects toward building, updating and maintaining large-scale data stores (e.g., database systems), these enterprise desire better efficiency without adding significant computing infrastructure.
Instead of submitting the database queries as batch jobs and waiting for a response, conventional large-scale data stores may accelerate database query execution and/or a related statistical analysis using any known incremental query execution technique. For instance, by generating incomplete query responses at an initial iteration and incrementally updating the incomplete query responses at each subsequent iteration until a final, complete response is produced, a user may view the progression of incomplete query responses when, after each iteration, updated query response data is rendered on a computer display.
Various visualization mechanisms permit monitoring of incremental query execution on structured enterprise data that is maintained across numerous storage systems. Present day enterprise-level systems implementing such mechanisms also may present statistical data that summarizes the structured enterprise data and/or expresses comparison results between portions of the structured enterprise data through, for example, histogram or chart representations. Some systems provide histograms conveying statistical data that is based upon hypothetical results. Because these hypothetical results do not accurately reflect actual results from the enterprise-level data store, engineers cannot use the hypothetical results to derive meaningful insight for improving the enterprise-level systems.